Video surveillance is an integral part of the technology used in modern day security systems. Known security systems can include surveillance cameras, video recorders, and video viewers so that surveillance cameras or other data collection devices monitor a particular region. Video data streams or image data from the cameras can be displayed and monitored by security personnel on video viewers or monitors, and the video or images can be stored in associated video recorders or other data storage devices.
One aspect of video surveillance is detecting objects of interest, including people, in the video data streams or image data. Systems and methods have been developed to detect people in video data streams or image data. For example, U.S. application Ser. No. 11/870,237 filed Oct. 10, 2007 titled “People Detection in Video and Image Data” discloses systems and methods to detect a person in image data. U.S. application Ser. No. 11/870,237 is assigned to the assignee hereof and is hereby incorporated by reference.
Detecting people in video data streams or image data present various challenges. For example, problems arise when detecting people in different poses and articulations. Because people move in fluid motions, the shapes of people are virtually endless. Additionally, there is often a large variation in the appearance of people both globally and locally because of changes in clothing style and/or camera angles. Finally, a monitored region may be crowded, which may cause occlusion between people. Occlusion between people, or other barriers between a person and a camera, may significantly complicate detecting the person.
In view of the above, there is a continuing, ongoing need for improved systems and methods of detecting people in video data streams or image data. Preferably, such systems and methods efficiently and effectively detect people in various poses, positions, and shapes and in crowded monitored regions.